Abstract: This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton's walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.

ICRA 2020 Conference Paper: available on arXiv

Contact: mtucker {at} caltech {dot} edu and/or enovoseller {at} caltech {dot} edu for more information

CoSpar Code: available on github

Related Research Media: CNBC Special

Bibtex:

@article{tucker2019preference,

title={Preference-Based Learning for Exoskeleton Gait Optimization},

author={Tucker, Maegan and Novoseller, Ellen and Kann, Claudia and Sui, Yanan and Yue, Yisong and Burdick, Joel and Ames, Aaron D},

journal={2020 IEEE International Conference on Robotics and Automation (ICRA)},

year={2020}

url={https://arxiv.org/abs/1909.12316}

}